Paper Info
Title
Split File Model for Big Data in Low Throughput Storage
Abstract
The demand for low-cost, large-scale storage is increasing. Recently, several low-throughput storage services such as the Pogo plug Cloud have been developed. These services are based on Amazon Glacier. They have low throughput, but low cost and large capacity. Therefore, these services are suitable for backups or archiving big data and can be used instead of offline storage tiers. To utilize such low throughput storage efficiently, we need tools for effective deduplication and resumable transfers, amongst others. We propose a split file model that can represent big data efficiently in low throughput storage. In the split file model, a large file is divided into many small parts, which are stored in a directory. We have developed tool commands to support the use of split files in a transparent way. Using these commands, replicated data is naturally excluded and effective shallow copying is supported. In this paper, we describe the split file model in detail and evaluate an implementation thereof.
Year
DOI
Venue
2013
10.1109/CISIS.2013.48
CISIS
Keywords
Field
DocType
large file,big data,split file model,low throughput,low-throughput storage service,low cost,split file,large-scale storage,offline storage tier,low throughput storage,information retrieval systems,throughput,cloud computing,information management,servers,data handling,data models
Data deduplication,Object storage,Converged storage,Data striping,Computer science,Directory,Computer network,Information repository,Storage area network,Operating system,Cloud computing
Conference
Citations 
PageRank 
References 
2
0.63
0
Authors
1
Name
Order
Citations
PageRank
Minoru Uehara152596.87